Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations121856
Missing cells395817
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.4 MiB
Average record size in memory1.1 KiB

Variable types

Numeric11
Unsupported9
Categorical17
Boolean2
Text1

Alerts

Car_Owned is highly overall correlated with Own_House_AgeHigh correlation
Child_Count is highly overall correlated with Client_Family_MembersHigh correlation
Client_Family_Members is highly overall correlated with Child_CountHigh correlation
Client_Occupation is highly overall correlated with Mobile_TagHigh correlation
Credit_Bureau is highly overall correlated with Mobile_TagHigh correlation
Mobile_Tag is highly overall correlated with Client_Occupation and 4 other fieldsHigh correlation
Own_House_Age is highly overall correlated with Car_OwnedHigh correlation
Phone_Change is highly overall correlated with Mobile_TagHigh correlation
Score_Source_1 is highly overall correlated with Mobile_TagHigh correlation
Score_Source_2 is highly overall correlated with Mobile_TagHigh correlation
Accompany_Client is highly imbalanced (66.3%) Imbalance
Client_Education is highly imbalanced (52.8%) Imbalance
Loan_Contract_Type is highly imbalanced (55.1%) Imbalance
Client_Housing_Type is highly imbalanced (72.2%) Imbalance
Mobile_Tag is highly imbalanced (> 99.9%) Imbalance
Client_Permanent_Match_Tag is highly imbalanced (60.8%) Imbalance
Default is highly imbalanced (59.5%) Imbalance
Client_Income has 3607 (3.0%) missing values Missing
Car_Owned has 3581 (2.9%) missing values Missing
Bike_Owned has 3624 (3.0%) missing values Missing
Active_Loan has 3635 (3.0%) missing values Missing
House_Own has 3661 (3.0%) missing values Missing
Child_Count has 3638 (3.0%) missing values Missing
Credit_Amount has 3632 (3.0%) missing values Missing
Loan_Annuity has 4812 (3.9%) missing values Missing
Accompany_Client has 1746 (1.4%) missing values Missing
Client_Income_Type has 3701 (3.0%) missing values Missing
Client_Education has 3645 (3.0%) missing values Missing
Client_Marital_Status has 3473 (2.9%) missing values Missing
Client_Gender has 2413 (2.0%) missing values Missing
Loan_Contract_Type has 3651 (3.0%) missing values Missing
Client_Housing_Type has 3687 (3.0%) missing values Missing
Population_Region_Relative has 4857 (4.0%) missing values Missing
Age_Days has 3600 (3.0%) missing values Missing
Employed_Days has 3649 (3.0%) missing values Missing
Registration_Days has 3614 (3.0%) missing values Missing
ID_Days has 5968 (4.9%) missing values Missing
Own_House_Age has 80095 (65.7%) missing values Missing
Client_Occupation has 41435 (34.0%) missing values Missing
Client_Family_Members has 2410 (2.0%) missing values Missing
Cleint_City_Rating has 2409 (2.0%) missing values Missing
Application_Process_Day has 2428 (2.0%) missing values Missing
Application_Process_Hour has 3663 (3.0%) missing values Missing
Type_Organization has 3609 (3.0%) missing values Missing
Score_Source_1 has 68835 (56.5%) missing values Missing
Score_Source_2 has 5686 (4.7%) missing values Missing
Score_Source_3 has 26921 (22.1%) missing values Missing
Social_Circle_Default has 61928 (50.8%) missing values Missing
Phone_Change has 3664 (3.0%) missing values Missing
Credit_Bureau has 18540 (15.2%) missing values Missing
Score_Source_2 is highly skewed (γ1 = 125.3457577) Skewed
ID is uniformly distributed Uniform
ID has unique values Unique
Client_Income is an unsupported type, check if it needs cleaning or further analysis Unsupported
Credit_Amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
Loan_Annuity is an unsupported type, check if it needs cleaning or further analysis Unsupported
Population_Region_Relative is an unsupported type, check if it needs cleaning or further analysis Unsupported
Age_Days is an unsupported type, check if it needs cleaning or further analysis Unsupported
Employed_Days is an unsupported type, check if it needs cleaning or further analysis Unsupported
Registration_Days is an unsupported type, check if it needs cleaning or further analysis Unsupported
ID_Days is an unsupported type, check if it needs cleaning or further analysis Unsupported
Score_Source_3 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Child_Count has 82834 (68.0%) zeros Zeros
Application_Process_Day has 6287 (5.2%) zeros Zeros
Phone_Change has 14555 (11.9%) zeros Zeros
Credit_Bureau has 28003 (23.0%) zeros Zeros

Reproduction

Analysis started2025-06-23 01:58:19.205562
Analysis finished2025-06-23 01:59:08.758891
Duration49.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct121856
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12160928
Minimum12100001
Maximum12221856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:09.109514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12100001
5-th percentile12106094
Q112130465
median12160928
Q312191392
95-th percentile12215763
Maximum12221856
Range121855
Interquartile range (IQR)60927.5

Descriptive statistics

Standard deviation35176.942
Coefficient of variation (CV)0.0028926197
Kurtosis-1.2
Mean12160928
Median Absolute Deviation (MAD)30464
Skewness6.127353 × 10-19
Sum1.4818821 × 1012
Variance1.2374172 × 109
MonotonicityNot monotonic
2025-06-23T07:29:09.292375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12201243 1
 
< 0.1%
12179258 1
 
< 0.1%
12151712 1
 
< 0.1%
12184641 1
 
< 0.1%
12183295 1
 
< 0.1%
12215564 1
 
< 0.1%
12220776 1
 
< 0.1%
12129827 1
 
< 0.1%
12182454 1
 
< 0.1%
12153526 1
 
< 0.1%
Other values (121846) 121846
> 99.9%
ValueCountFrequency (%)
12100001 1
< 0.1%
12100002 1
< 0.1%
12100003 1
< 0.1%
12100004 1
< 0.1%
12100005 1
< 0.1%
12100006 1
< 0.1%
12100007 1
< 0.1%
12100008 1
< 0.1%
12100009 1
< 0.1%
12100010 1
< 0.1%
ValueCountFrequency (%)
12221856 1
< 0.1%
12221855 1
< 0.1%
12221854 1
< 0.1%
12221853 1
< 0.1%
12221852 1
< 0.1%
12221851 1
< 0.1%
12221850 1
< 0.1%
12221849 1
< 0.1%
12221848 1
< 0.1%
12221847 1
< 0.1%

Client_Income
Unsupported

Missing  Rejected  Unsupported 

Missing3607
Missing (%)3.0%
Memory size5.8 MiB

Car_Owned
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing3581
Missing (%)2.9%
Memory size6.1 MiB
0.0
77724 
1.0
40551 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354825
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 77724
63.8%
1.0 40551
33.3%
(Missing) 3581
 
2.9%

Length

2025-06-23T07:29:09.441847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:09.492357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77724
65.7%
1.0 40551
34.3%

Most occurring characters

ValueCountFrequency (%)
0 195999
55.2%
. 118275
33.3%
1 40551
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 195999
55.2%
. 118275
33.3%
1 40551
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 195999
55.2%
. 118275
33.3%
1 40551
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 195999
55.2%
. 118275
33.3%
1 40551
 
11.4%

Bike_Owned
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3624
Missing (%)3.0%
Memory size6.1 MiB
0.0
78948 
1.0
39284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354696
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 78948
64.8%
1.0 39284
32.2%
(Missing) 3624
 
3.0%

Length

2025-06-23T07:29:09.585264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:09.676287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 78948
66.8%
1.0 39284
33.2%

Most occurring characters

ValueCountFrequency (%)
0 197180
55.6%
. 118232
33.3%
1 39284
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 197180
55.6%
. 118232
33.3%
1 39284
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 197180
55.6%
. 118232
33.3%
1 39284
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 197180
55.6%
. 118232
33.3%
1 39284
 
11.1%

Active_Loan
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3635
Missing (%)3.0%
Memory size6.1 MiB
0.0
59208 
1.0
59013 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354663
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 59208
48.6%
1.0 59013
48.4%
(Missing) 3635
 
3.0%

Length

2025-06-23T07:29:09.809106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:10.076238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59208
50.1%
1.0 59013
49.9%

Most occurring characters

ValueCountFrequency (%)
0 177429
50.0%
. 118221
33.3%
1 59013
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177429
50.0%
. 118221
33.3%
1 59013
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177429
50.0%
. 118221
33.3%
1 59013
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177429
50.0%
. 118221
33.3%
1 59013
 
16.6%

House_Own
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3661
Missing (%)3.0%
Memory size6.1 MiB
1.0
81798 
0.0
36397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354585
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 81798
67.1%
0.0 36397
29.9%
(Missing) 3661
 
3.0%

Length

2025-06-23T07:29:10.145585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:10.217705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 81798
69.2%
0.0 36397
30.8%

Most occurring characters

ValueCountFrequency (%)
0 154592
43.6%
. 118195
33.3%
1 81798
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 154592
43.6%
. 118195
33.3%
1 81798
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 154592
43.6%
. 118195
33.3%
1 81798
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 154592
43.6%
. 118195
33.3%
1 81798
23.1%

Child_Count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14
Distinct (%)< 0.1%
Missing3638
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean0.41777902
Minimum0
Maximum19
Zeros82834
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:10.325766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72880243
Coefficient of variation (CV)1.7444687
Kurtosis12.161459
Mean0.41777902
Median Absolute Deviation (MAD)0
Skewness2.191615
Sum49389
Variance0.53115298
MonotonicityNot monotonic
2025-06-23T07:29:10.425524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 82834
68.0%
1 23431
 
19.2%
2 10294
 
8.4%
3 1430
 
1.2%
4 167
 
0.1%
5 34
 
< 0.1%
6 12
 
< 0.1%
7 4
 
< 0.1%
14 4
 
< 0.1%
10 3
 
< 0.1%
Other values (4) 5
 
< 0.1%
(Missing) 3638
 
3.0%
ValueCountFrequency (%)
0 82834
68.0%
1 23431
 
19.2%
2 10294
 
8.4%
3 1430
 
1.2%
4 167
 
0.1%
5 34
 
< 0.1%
6 12
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 4
 
< 0.1%
12 1
 
< 0.1%
10 3
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 12
 
< 0.1%
5 34
 
< 0.1%
4 167
0.1%

Credit_Amount
Unsupported

Missing  Rejected  Unsupported 

Missing3632
Missing (%)3.0%
Memory size4.6 MiB

Loan_Annuity
Unsupported

Missing  Rejected  Unsupported 

Missing4812
Missing (%)3.9%
Memory size5.8 MiB

Accompany_Client
Categorical

Imbalance  Missing 

Distinct7
Distinct (%)< 0.1%
Missing1746
Missing (%)1.4%
Memory size6.3 MiB
Alone
97409 
Relative
15748 
Partner
 
4516
Kids
 
1334
Others
 
987
Other values (2)
 
116

Length

Max length8
Median length5
Mean length5.4653484
Min length2

Characters and Unicode

Total characters656443
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowAlone
3rd rowAlone
4th rowAlone
5th rowAlone

Common Values

ValueCountFrequency (%)
Alone 97409
79.9%
Relative 15748
 
12.9%
Partner 4516
 
3.7%
Kids 1334
 
1.1%
Others 987
 
0.8%
Group 104
 
0.1%
## 12
 
< 0.1%
(Missing) 1746
 
1.4%

Length

2025-06-23T07:29:10.525776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:10.625427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alone 97409
81.1%
relative 15748
 
13.1%
partner 4516
 
3.8%
kids 1334
 
1.1%
others 987
 
0.8%
group 104
 
0.1%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 134408
20.5%
l 113157
17.2%
n 101925
15.5%
o 97513
14.9%
A 97409
14.8%
t 21251
 
3.2%
a 20264
 
3.1%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 656443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 134408
20.5%
l 113157
17.2%
n 101925
15.5%
o 97513
14.9%
A 97409
14.8%
t 21251
 
3.2%
a 20264
 
3.1%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 656443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 134408
20.5%
l 113157
17.2%
n 101925
15.5%
o 97513
14.9%
A 97409
14.8%
t 21251
 
3.2%
a 20264
 
3.1%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 656443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 134408
20.5%
l 113157
17.2%
n 101925
15.5%
o 97513
14.9%
A 97409
14.8%
t 21251
 
3.2%
a 20264
 
3.1%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Client_Income_Type
Categorical

Missing 

Distinct8
Distinct (%)< 0.1%
Missing3701
Missing (%)3.0%
Memory size6.6 MiB
Service
61028 
Commercial
27764 
Retired
21043 
Govt Job
8303 
Student
 
8
Other values (3)
 
9

Length

Max length15
Median length7
Mean length7.7755321
Min length7

Characters and Unicode

Total characters918718
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCommercial
2nd rowService
3rd rowService
4th rowRetired
5th rowCommercial

Common Values

ValueCountFrequency (%)
Service 61028
50.1%
Commercial 27764
22.8%
Retired 21043
 
17.3%
Govt Job 8303
 
6.8%
Student 8
 
< 0.1%
Unemployed 6
 
< 0.1%
Maternity leave 2
 
< 0.1%
Businessman 1
 
< 0.1%
(Missing) 3701
 
3.0%

Length

2025-06-23T07:29:10.759555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:10.897109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
service 61028
48.3%
commercial 27764
22.0%
retired 21043
 
16.6%
govt 8303
 
6.6%
job 8303
 
6.6%
student 8
 
< 0.1%
unemployed 6
 
< 0.1%
maternity 2
 
< 0.1%
leave 2
 
< 0.1%
businessman 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 191933
20.9%
i 109838
12.0%
r 109837
12.0%
c 88792
9.7%
v 69333
 
7.5%
S 61036
 
6.6%
m 55535
 
6.0%
o 44376
 
4.8%
t 29366
 
3.2%
l 27772
 
3.0%
Other values (16) 130900
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 918718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 191933
20.9%
i 109838
12.0%
r 109837
12.0%
c 88792
9.7%
v 69333
 
7.5%
S 61036
 
6.6%
m 55535
 
6.0%
o 44376
 
4.8%
t 29366
 
3.2%
l 27772
 
3.0%
Other values (16) 130900
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 918718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 191933
20.9%
i 109838
12.0%
r 109837
12.0%
c 88792
9.7%
v 69333
 
7.5%
S 61036
 
6.6%
m 55535
 
6.0%
o 44376
 
4.8%
t 29366
 
3.2%
l 27772
 
3.0%
Other values (16) 130900
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 918718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 191933
20.9%
i 109838
12.0%
r 109837
12.0%
c 88792
9.7%
v 69333
 
7.5%
S 61036
 
6.6%
m 55535
 
6.0%
o 44376
 
4.8%
t 29366
 
3.2%
l 27772
 
3.0%
Other values (16) 130900
14.2%

Client_Education
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing3645
Missing (%)3.0%
Memory size6.8 MiB
Secondary
83911 
Graduation
28819 
Graduation dropout
 
3960
Junior secondary
 
1455
Post Grad
 
66

Length

Max length18
Median length9
Mean length9.6314472
Min length9

Characters and Unicode

Total characters1138543
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary
2nd rowGraduation
3rd rowGraduation dropout
4th rowSecondary
5th rowSecondary

Common Values

ValueCountFrequency (%)
Secondary 83911
68.9%
Graduation 28819
 
23.7%
Graduation dropout 3960
 
3.2%
Junior secondary 1455
 
1.2%
Post Grad 66
 
0.1%
(Missing) 3645
 
3.0%

Length

2025-06-23T07:29:11.060024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:11.145309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
secondary 85366
69.0%
graduation 32779
 
26.5%
dropout 3960
 
3.2%
junior 1455
 
1.2%
post 66
 
0.1%
grad 66
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 150990
13.3%
o 127586
11.2%
r 123626
10.9%
d 122171
10.7%
n 119600
10.5%
e 85366
7.5%
y 85366
7.5%
c 85366
7.5%
S 83911
7.4%
u 38194
 
3.4%
Other values (8) 116367
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1138543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 150990
13.3%
o 127586
11.2%
r 123626
10.9%
d 122171
10.7%
n 119600
10.5%
e 85366
7.5%
y 85366
7.5%
c 85366
7.5%
S 83911
7.4%
u 38194
 
3.4%
Other values (8) 116367
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1138543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 150990
13.3%
o 127586
11.2%
r 123626
10.9%
d 122171
10.7%
n 119600
10.5%
e 85366
7.5%
y 85366
7.5%
c 85366
7.5%
S 83911
7.4%
u 38194
 
3.4%
Other values (8) 116367
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1138543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 150990
13.3%
o 127586
11.2%
r 123626
10.9%
d 122171
10.7%
n 119600
10.5%
e 85366
7.5%
y 85366
7.5%
c 85366
7.5%
S 83911
7.4%
u 38194
 
3.4%
Other values (8) 116367
10.2%

Client_Marital_Status
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing3473
Missing (%)2.9%
Memory size5.8 MiB
M
87349 
S
17404 
D
 
7556
W
 
6074

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters118383
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowW
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 87349
71.7%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%
(Missing) 3473
 
2.9%

Length

2025-06-23T07:29:11.228633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:11.311757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 87349
73.8%
s 17404
 
14.7%
d 7556
 
6.4%
w 6074
 
5.1%

Most occurring characters

ValueCountFrequency (%)
M 87349
73.8%
S 17404
 
14.7%
D 7556
 
6.4%
W 6074
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 87349
73.8%
S 17404
 
14.7%
D 7556
 
6.4%
W 6074
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 87349
73.8%
S 17404
 
14.7%
D 7556
 
6.4%
W 6074
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 87349
73.8%
S 17404
 
14.7%
D 7556
 
6.4%
W 6074
 
5.1%

Client_Gender
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2413
Missing (%)2.0%
Memory size6.2 MiB
Male
78463 
Female
40977 
XNA
 
3

Length

Max length6
Median length4
Mean length4.6861097
Min length3

Characters and Unicode

Total characters559723
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 78463
64.4%
Female 40977
33.6%
XNA 3
 
< 0.1%
(Missing) 2413
 
2.0%

Length

2025-06-23T07:29:11.392887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:11.459047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 78463
65.7%
female 40977
34.3%
xna 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 160417
28.7%
l 119440
21.3%
a 119440
21.3%
M 78463
14.0%
F 40977
 
7.3%
m 40977
 
7.3%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 559723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 160417
28.7%
l 119440
21.3%
a 119440
21.3%
M 78463
14.0%
F 40977
 
7.3%
m 40977
 
7.3%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 559723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 160417
28.7%
l 119440
21.3%
a 119440
21.3%
M 78463
14.0%
F 40977
 
7.3%
m 40977
 
7.3%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 559723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 160417
28.7%
l 119440
21.3%
a 119440
21.3%
M 78463
14.0%
F 40977
 
7.3%
m 40977
 
7.3%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Loan_Contract_Type
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing3651
Missing (%)3.0%
Memory size5.9 MiB
CL
107118 
RL
11087 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236410
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCL
2nd rowCL
3rd rowCL
4th rowCL
5th rowCL

Common Values

ValueCountFrequency (%)
CL 107118
87.9%
RL 11087
 
9.1%
(Missing) 3651
 
3.0%

Length

2025-06-23T07:29:11.547789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:11.603583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cl 107118
90.6%
rl 11087
 
9.4%

Most occurring characters

ValueCountFrequency (%)
L 118205
50.0%
C 107118
45.3%
R 11087
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 118205
50.0%
C 107118
45.3%
R 11087
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 118205
50.0%
C 107118
45.3%
R 11087
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 118205
50.0%
C 107118
45.3%
R 11087
 
4.7%

Client_Housing_Type
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)< 0.1%
Missing3687
Missing (%)3.0%
Memory size6.2 MiB
Home
104870 
Family
 
5783
Municipal
 
4248
Rental
 
1816
Office
 
1002

Length

Max length9
Median length4
Mean length4.33293
Min length4

Characters and Unicode

Total characters512018
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowFamily
4th rowHome
5th rowHome

Common Values

ValueCountFrequency (%)
Home 104870
86.1%
Family 5783
 
4.7%
Municipal 4248
 
3.5%
Rental 1816
 
1.5%
Office 1002
 
0.8%
Shared 450
 
0.4%
(Missing) 3687
 
3.0%

Length

2025-06-23T07:29:11.691930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:11.782507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home 104870
88.7%
family 5783
 
4.9%
municipal 4248
 
3.6%
rental 1816
 
1.5%
office 1002
 
0.8%
shared 450
 
0.4%

Most occurring characters

ValueCountFrequency (%)
m 110653
21.6%
e 108138
21.1%
o 104870
20.5%
H 104870
20.5%
i 15281
 
3.0%
a 12297
 
2.4%
l 11847
 
2.3%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 110653
21.6%
e 108138
21.1%
o 104870
20.5%
H 104870
20.5%
i 15281
 
3.0%
a 12297
 
2.4%
l 11847
 
2.3%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 110653
21.6%
e 108138
21.1%
o 104870
20.5%
H 104870
20.5%
i 15281
 
3.0%
a 12297
 
2.4%
l 11847
 
2.3%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 110653
21.6%
e 108138
21.1%
o 104870
20.5%
H 104870
20.5%
i 15281
 
3.0%
a 12297
 
2.4%
l 11847
 
2.3%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.2%

Population_Region_Relative
Unsupported

Missing  Rejected  Unsupported 

Missing4857
Missing (%)4.0%
Memory size5.6 MiB

Age_Days
Unsupported

Missing  Rejected  Unsupported 

Missing3600
Missing (%)3.0%
Memory size6.0 MiB

Employed_Days
Unsupported

Missing  Rejected  Unsupported 

Missing3649
Missing (%)3.0%
Memory size6.0 MiB

Registration_Days
Unsupported

Missing  Rejected  Unsupported 

Missing3614
Missing (%)3.0%
Memory size5.9 MiB

ID_Days
Unsupported

Missing  Rejected  Unsupported 

Missing5968
Missing (%)4.9%
Memory size5.9 MiB

Own_House_Age
Real number (ℝ)

High correlation  Missing 

Distinct55
Distinct (%)0.1%
Missing80095
Missing (%)65.7%
Infinite0
Infinite (%)0.0%
Mean12.157324
Minimum0
Maximum69
Zeros859
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:11.890387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q315
95-th percentile30
Maximum69
Range69
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.056079
Coefficient of variation (CV)0.99167215
Kurtosis8.9863734
Mean12.157324
Median Absolute Deviation (MAD)5
Skewness2.724026
Sum507702
Variance145.34905
MonotonicityNot monotonic
2025-06-23T07:29:12.046218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 3015
 
2.5%
3 2555
 
2.1%
6 2525
 
2.1%
2 2321
 
1.9%
8 2302
 
1.9%
4 2175
 
1.8%
9 2053
 
1.7%
1 2041
 
1.7%
10 1945
 
1.6%
14 1855
 
1.5%
Other values (45) 18974
 
15.6%
(Missing) 80095
65.7%
ValueCountFrequency (%)
0 859
 
0.7%
1 2041
1.7%
2 2321
1.9%
3 2555
2.1%
4 2175
1.8%
5 1433
1.2%
6 2525
2.1%
7 3015
2.5%
8 2302
1.9%
9 2053
1.7%
ValueCountFrequency (%)
69 1
 
< 0.1%
65 392
0.3%
64 974
0.8%
63 2
 
< 0.1%
57 2
 
< 0.1%
54 5
 
< 0.1%
50 2
 
< 0.1%
49 1
 
< 0.1%
46 2
 
< 0.1%
45 1
 
< 0.1%

Mobile_Tag
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
1
121855 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Length

2025-06-23T07:29:12.150174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:12.226262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 121855
> 99.9%
0 1
 
< 0.1%

Homephone_Tag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
97424 
1
24432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%

Length

2025-06-23T07:29:12.339711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:12.420940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97424
80.0%
1 24432
 
20.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
87590 
1
34266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Length

2025-06-23T07:29:12.553604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:12.619994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Client_Occupation
Categorical

High correlation  Missing 

Distinct18
Distinct (%)< 0.1%
Missing41435
Missing (%)34.0%
Memory size6.6 MiB
Laborers
21024 
Sales
12136 
Core
10611 
Managers
8099 
Drivers
7150 
Other values (13)
21401 

Length

Max length18
Median length15
Mean length7.649967
Min length2

Characters and Unicode

Total characters615218
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowRealty agents
3rd rowLaborers
4th rowLaborers
5th rowSales

Common Values

ValueCountFrequency (%)
Laborers 21024
17.3%
Sales 12136
 
10.0%
Core 10611
 
8.7%
Managers 8099
 
6.6%
Drivers 7150
 
5.9%
High skill tech 4317
 
3.5%
Accountants 3766
 
3.1%
Medicine 3172
 
2.6%
Security 2683
 
2.2%
Cooking 2224
 
1.8%
Other values (8) 5239
 
4.3%
(Missing) 41435
34.0%

Length

2025-06-23T07:29:12.696712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
laborers 21811
23.9%
sales 12136
13.3%
core 10611
11.6%
managers 8099
 
8.9%
drivers 7150
 
7.8%
high 4317
 
4.7%
skill 4317
 
4.7%
tech 4317
 
4.7%
accountants 3766
 
4.1%
medicine 3172
 
3.5%
Other values (12) 11432
12.5%

Most occurring characters

ValueCountFrequency (%)
r 83411
13.6%
e 81066
13.2%
s 60394
 
9.8%
a 58752
 
9.5%
o 41423
 
6.7%
i 32598
 
5.3%
n 25262
 
4.1%
l 24346
 
4.0%
L 22598
 
3.7%
b 22322
 
3.6%
Other values (25) 163046
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 83411
13.6%
e 81066
13.2%
s 60394
 
9.8%
a 58752
 
9.5%
o 41423
 
6.7%
i 32598
 
5.3%
n 25262
 
4.1%
l 24346
 
4.0%
L 22598
 
3.7%
b 22322
 
3.6%
Other values (25) 163046
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 83411
13.6%
e 81066
13.2%
s 60394
 
9.8%
a 58752
 
9.5%
o 41423
 
6.7%
i 32598
 
5.3%
n 25262
 
4.1%
l 24346
 
4.0%
L 22598
 
3.7%
b 22322
 
3.6%
Other values (25) 163046
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 83411
13.6%
e 81066
13.2%
s 60394
 
9.8%
a 58752
 
9.5%
o 41423
 
6.7%
i 32598
 
5.3%
n 25262
 
4.1%
l 24346
 
4.0%
L 22598
 
3.7%
b 22322
 
3.6%
Other values (25) 163046
26.5%

Client_Family_Members
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)< 0.1%
Missing2410
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.1543292
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:12.771469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91268564
Coefficient of variation (CV)0.4236519
Kurtosis3.0826497
Mean2.1543292
Median Absolute Deviation (MAD)0
Skewness1.0338185
Sum257326
Variance0.83299507
MonotonicityNot monotonic
2025-06-23T07:29:12.849195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 61652
50.6%
1 26213
21.5%
3 20434
 
16.8%
4 9583
 
7.9%
5 1349
 
1.1%
6 157
 
0.1%
7 32
 
< 0.1%
8 11
 
< 0.1%
9 4
 
< 0.1%
10 3
 
< 0.1%
Other values (5) 8
 
< 0.1%
(Missing) 2410
 
2.0%
ValueCountFrequency (%)
1 26213
21.5%
2 61652
50.6%
3 20434
 
16.8%
4 9583
 
7.9%
5 1349
 
1.1%
6 157
 
0.1%
7 32
 
< 0.1%
8 11
 
< 0.1%
9 4
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 3
 
< 0.1%
10 3
 
< 0.1%
9 4
 
< 0.1%
8 11
 
< 0.1%
7 32
 
< 0.1%
6 157
0.1%

Cleint_City_Rating
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2409
Missing (%)2.0%
Memory size6.1 MiB
2.0
88949 
3.0
17043 
1.0
13455 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters358341
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 88949
73.0%
3.0 17043
 
14.0%
1.0 13455
 
11.0%
(Missing) 2409
 
2.0%

Length

2025-06-23T07:29:12.945540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:13.059700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 88949
74.5%
3.0 17043
 
14.3%
1.0 13455
 
11.3%

Most occurring characters

ValueCountFrequency (%)
. 119447
33.3%
0 119447
33.3%
2 88949
24.8%
3 17043
 
4.8%
1 13455
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 119447
33.3%
0 119447
33.3%
2 88949
24.8%
3 17043
 
4.8%
1 13455
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 119447
33.3%
0 119447
33.3%
2 88949
24.8%
3 17043
 
4.8%
1 13455
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 119447
33.3%
0 119447
33.3%
2 88949
24.8%
3 17043
 
4.8%
1 13455
 
3.8%

Application_Process_Day
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing2428
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.1597364
Minimum0
Maximum6
Zeros6287
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:13.159535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.759045
Coefficient of variation (CV)0.55670624
Kurtosis-1.0914392
Mean3.1597364
Median Absolute Deviation (MAD)1
Skewness0.007727949
Sum377361
Variance3.0942392
MonotonicityNot monotonic
2025-06-23T07:29:13.259571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 20907
17.2%
3 20116
16.5%
1 19712
16.2%
4 19668
16.1%
5 19613
16.1%
6 13125
10.8%
0 6287
 
5.2%
(Missing) 2428
 
2.0%
ValueCountFrequency (%)
0 6287
 
5.2%
1 19712
16.2%
2 20907
17.2%
3 20116
16.5%
4 19668
16.1%
5 19613
16.1%
6 13125
10.8%
ValueCountFrequency (%)
6 13125
10.8%
5 19613
16.1%
4 19668
16.1%
3 20116
16.5%
2 20907
17.2%
1 19712
16.2%
0 6287
 
5.2%

Application_Process_Hour
Real number (ℝ)

Missing 

Distinct24
Distinct (%)< 0.1%
Missing3663
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean12.0631
Minimum0
Maximum23
Zeros26
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:13.341884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile17
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2806946
Coefficient of variation (CV)0.27196115
Kurtosis-0.18198285
Mean12.0631
Median Absolute Deviation (MAD)2
Skewness-0.034235866
Sum1425774
Variance10.762957
MonotonicityNot monotonic
2025-06-23T07:29:13.446030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 14465
11.9%
11 14413
11.8%
12 12977
10.6%
13 11765
9.7%
14 10702
8.8%
9 10525
8.6%
15 9614
7.9%
16 7739
6.4%
17 5843
4.8%
8 5821
4.8%
Other values (14) 14329
11.8%
(Missing) 3663
 
3.0%
ValueCountFrequency (%)
0 26
 
< 0.1%
1 28
 
< 0.1%
2 112
 
0.1%
3 506
 
0.4%
4 854
 
0.7%
5 1437
 
1.2%
6 2247
 
1.8%
7 3441
 
2.8%
8 5821
4.8%
9 10525
8.6%
ValueCountFrequency (%)
23 14
 
< 0.1%
22 67
 
0.1%
21 164
 
0.1%
20 494
 
0.4%
19 1464
 
1.2%
18 3475
 
2.9%
17 5843
4.8%
16 7739
6.4%
15 9614
7.9%
14 10702
8.8%

Client_Permanent_Match_Tag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
112454 
False
 
9402
ValueCountFrequency (%)
True 112454
92.3%
False 9402
 
7.7%
2025-06-23T07:29:13.521917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
100015 
False
21841 
ValueCountFrequency (%)
True 100015
82.1%
False 21841
 
17.9%
2025-06-23T07:29:13.553324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type_Organization
Text

Missing 

Distinct58
Distinct (%)< 0.1%
Missing3609
Missing (%)3.0%
Memory size7.1 MiB
2025-06-23T07:29:13.741989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length17
Mean length12.557105
Min length3

Characters and Unicode

Total characters1484840
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-employed
2nd rowGovernment
3rd rowSelf-employed
4th rowXNA
5th rowBusiness Entity Type 3
ValueCountFrequency (%)
type 47072
19.1%
business 32718
13.3%
entity 32718
13.3%
3 29353
11.9%
xna 21085
8.6%
self-employed 14725
 
6.0%
other 6290
 
2.6%
2 5826
 
2.4%
trade 5458
 
2.2%
industry 5431
 
2.2%
Other values (40) 45308
18.4%
2025-06-23T07:29:13.992604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 167728
 
11.3%
127737
 
8.6%
t 116094
 
7.8%
s 115506
 
7.8%
y 104149
 
7.0%
n 102473
 
6.9%
i 92019
 
6.2%
p 65262
 
4.4%
u 47183
 
3.2%
r 45109
 
3.0%
Other values (42) 501580
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1484840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 167728
 
11.3%
127737
 
8.6%
t 116094
 
7.8%
s 115506
 
7.8%
y 104149
 
7.0%
n 102473
 
6.9%
i 92019
 
6.2%
p 65262
 
4.4%
u 47183
 
3.2%
r 45109
 
3.0%
Other values (42) 501580
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1484840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 167728
 
11.3%
127737
 
8.6%
t 116094
 
7.8%
s 115506
 
7.8%
y 104149
 
7.0%
n 102473
 
6.9%
i 92019
 
6.2%
p 65262
 
4.4%
u 47183
 
3.2%
r 45109
 
3.0%
Other values (42) 501580
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1484840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 167728
 
11.3%
127737
 
8.6%
t 116094
 
7.8%
s 115506
 
7.8%
y 104149
 
7.0%
n 102473
 
6.9%
i 92019
 
6.2%
p 65262
 
4.4%
u 47183
 
3.2%
r 45109
 
3.0%
Other values (42) 501580
33.8%

Score_Source_1
Real number (ℝ)

High correlation  Missing 

Distinct43968
Distinct (%)82.9%
Missing68835
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean0.50121293
Minimum0.014568132
Maximum0.94574129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:14.095332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.014568132
5-th percentile0.15787192
Q10.33348052
median0.50465669
Q30.67389006
95-th percentile0.83137473
Maximum0.94574129
Range0.93117316
Interquartile range (IQR)0.34040954

Descriptive statistics

Standard deviation0.21120445
Coefficient of variation (CV)0.42138668
Kurtosis-0.96855606
Mean0.50121293
Median Absolute Deviation (MAD)0.1702147
Skewness-0.067090432
Sum26574.81
Variance0.044607319
MonotonicityNot monotonic
2025-06-23T07:29:14.225734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.651465287 5
 
< 0.1%
0.270675413 5
 
< 0.1%
0.592852493 5
 
< 0.1%
0.533412711 5
 
< 0.1%
0.716766447 5
 
< 0.1%
0.468183846 5
 
< 0.1%
0.607615944 4
 
< 0.1%
0.563194838 4
 
< 0.1%
0.611171846 4
 
< 0.1%
0.748709986 4
 
< 0.1%
Other values (43958) 52975
43.5%
(Missing) 68835
56.5%
ValueCountFrequency (%)
0.014568132 1
< 0.1%
0.017176539 1
< 0.1%
0.017394409 1
< 0.1%
0.017896805 1
< 0.1%
0.018333565 2
< 0.1%
0.01919138 1
< 0.1%
0.019495375 1
< 0.1%
0.020057908 1
< 0.1%
0.022139338 1
< 0.1%
0.022321291 1
< 0.1%
ValueCountFrequency (%)
0.945741288 2
< 0.1%
0.943982239 1
< 0.1%
0.942680454 2
< 0.1%
0.942333186 1
< 0.1%
0.941652145 1
< 0.1%
0.94143272 1
< 0.1%
0.93951178 1
< 0.1%
0.939097027 1
< 0.1%
0.93820318 1
< 0.1%
0.936037582 1
< 0.1%

Score_Source_2
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct67016
Distinct (%)57.7%
Missing5686
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean0.51862482
Minimum5 × 10-6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:14.359030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5 × 10-6
5-th percentile0.1320899
Q10.39016435
median0.56497814
Q30.66401116
95-th percentile0.74802998
Maximum100
Range99.999995
Interquartile range (IQR)0.27384681

Descriptive statistics

Standard deviation0.74024835
Coefficient of variation (CV)1.4273292
Kurtosis16844.718
Mean0.51862482
Median Absolute Deviation (MAD)0.12030174
Skewness125.34576
Sum60248.646
Variance0.54796762
MonotonicityNot monotonic
2025-06-23T07:29:14.659195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.285897872 269
 
0.2%
0.262258369 137
 
0.1%
0.159679234 135
 
0.1%
0.26525634 124
 
0.1%
0.265311748 116
 
0.1%
0.263143591 99
 
0.1%
0.162192106 87
 
0.1%
0.162144568 86
 
0.1%
0.160405321 85
 
0.1%
0.266519775 84
 
0.1%
Other values (67006) 114948
94.3%
(Missing) 5686
 
4.7%
ValueCountFrequency (%)
5 × 10-61
< 0.1%
1.64 × 10-51
< 0.1%
1.67 × 10-52
< 0.1%
1.69 × 10-52
< 0.1%
2.38 × 10-51
< 0.1%
3.01 × 10-51
< 0.1%
3.49 × 10-52
< 0.1%
7.36 × 10-51
< 0.1%
7.39 × 10-51
< 0.1%
8.99 × 10-51
< 0.1%
ValueCountFrequency (%)
100 6
< 0.1%
0.854999666 8
< 0.1%
0.821393627 1
 
< 0.1%
0.820609506 1
 
< 0.1%
0.820487147 1
 
< 0.1%
0.818575745 1
 
< 0.1%
0.818403562 1
 
< 0.1%
0.817367791 1
 
< 0.1%
0.815647763 1
 
< 0.1%
0.815601725 1
 
< 0.1%

Score_Source_3
Unsupported

Missing  Rejected  Unsupported 

Missing26921
Missing (%)22.1%
Memory size4.1 MiB

Social_Circle_Default
Real number (ℝ)

Missing 

Distinct1882
Distinct (%)3.1%
Missing61928
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean0.11742786
Minimum0
Maximum1
Zeros294
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:14.776066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0082
Q10.0577
median0.0887
Q30.1485
95-th percentile0.3258
Maximum1
Range1
Interquartile range (IQR)0.0908

Descriptive statistics

Standard deviation0.10797382
Coefficient of variation (CV)0.91949073
Kurtosis11.585799
Mean0.11742786
Median Absolute Deviation (MAD)0.0433
Skewness2.6564992
Sum7037.2165
Variance0.011658347
MonotonicityNot monotonic
2025-06-23T07:29:14.903624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0825 2648
 
2.2%
0.0619 2531
 
2.1%
0.0928 1763
 
1.4%
0.0722 1592
 
1.3%
0.0082 1397
 
1.1%
0.1031 1213
 
1.0%
0.0165 1207
 
1.0%
0.1485 1129
 
0.9%
0.0124 1070
 
0.9%
0.0742 897
 
0.7%
Other values (1872) 44481
36.5%
(Missing) 61928
50.8%
ValueCountFrequency (%)
0 294
0.2%
0.001 65
 
0.1%
0.0015 3
 
< 0.1%
0.0021 342
0.3%
0.0024 1
 
< 0.1%
0.0026 4
 
< 0.1%
0.0031 163
0.1%
0.0034 1
 
< 0.1%
0.0036 3
 
< 0.1%
0.0038 2
 
< 0.1%
ValueCountFrequency (%)
1 54
< 0.1%
0.9907 3
 
< 0.1%
0.9876 3
 
< 0.1%
0.9814 4
 
< 0.1%
0.9804 1
 
< 0.1%
0.9562 1
 
< 0.1%
0.9557 5
 
< 0.1%
0.9485 4
 
< 0.1%
0.9443 1
 
< 0.1%
0.9402 1
 
< 0.1%

Phone_Change
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3590
Distinct (%)3.0%
Missing3664
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean962.10606
Minimum0
Maximum4185
Zeros14555
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:15.065006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1272
median755
Q31570
95-th percentile2522
Maximum4185
Range4185
Interquartile range (IQR)1298

Descriptive statistics

Standard deviation827.97673
Coefficient of variation (CV)0.86058779
Kurtosis-0.30424654
Mean962.10606
Median Absolute Deviation (MAD)627
Skewness0.71654039
Sum1.1371324 × 108
Variance685545.46
MonotonicityNot monotonic
2025-06-23T07:29:15.209485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14555
 
11.9%
1 1104
 
0.9%
2 916
 
0.8%
3 645
 
0.5%
4 524
 
0.4%
5 322
 
0.3%
6 216
 
0.2%
7 179
 
0.1%
8 118
 
0.1%
448 94
 
0.1%
Other values (3580) 99519
81.7%
(Missing) 3664
 
3.0%
ValueCountFrequency (%)
0 14555
11.9%
1 1104
 
0.9%
2 916
 
0.8%
3 645
 
0.5%
4 524
 
0.4%
5 322
 
0.3%
6 216
 
0.2%
7 179
 
0.1%
8 118
 
0.1%
9 72
 
0.1%
ValueCountFrequency (%)
4185 1
< 0.1%
4153 1
< 0.1%
4128 2
< 0.1%
4121 1
< 0.1%
4092 2
< 0.1%
4070 2
< 0.1%
4051 1
< 0.1%
4033 2
< 0.1%
4021 1
< 0.1%
4020 1
< 0.1%

Credit_Bureau
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct21
Distinct (%)< 0.1%
Missing18540
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean1.8910817
Minimum0
Maximum22
Zeros28003
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-23T07:29:15.359968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum22
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8619212
Coefficient of variation (CV)0.98457999
Kurtosis2.1558673
Mean1.8910817
Median Absolute Deviation (MAD)1
Skewness1.2593187
Sum195379
Variance3.4667506
MonotonicityNot monotonic
2025-06-23T07:29:15.509417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 28003
23.0%
1 24572
20.2%
2 19606
16.1%
3 13102
10.8%
4 7978
 
6.5%
5 4671
 
3.8%
6 2660
 
2.2%
7 1421
 
1.2%
8 832
 
0.7%
9 422
 
0.3%
Other values (11) 49
 
< 0.1%
(Missing) 18540
15.2%
ValueCountFrequency (%)
0 28003
23.0%
1 24572
20.2%
2 19606
16.1%
3 13102
10.8%
4 7978
 
6.5%
5 4671
 
3.8%
6 2660
 
2.2%
7 1421
 
1.2%
8 832
 
0.7%
9 422
 
0.3%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 2
 
< 0.1%
19 5
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 3
 
< 0.1%
14 6
< 0.1%
13 6
< 0.1%
12 2
 
< 0.1%
11 11
< 0.1%

Default
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
112011 
1
 
9845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Length

2025-06-23T07:29:15.648878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-23T07:29:15.708728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Interactions

2025-06-23T07:29:02.506411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:47.791670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.417394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.705572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.250423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.887380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.559912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.025428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.528729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.995036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.279222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.693010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:47.959877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.533821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.816585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.380931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.075652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.725760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.112159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.641361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.124737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.408295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.842799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.093544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.627780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.977423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.542819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.208407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.886507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.251445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.809983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.231425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.508576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.992300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.264565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.728458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.091733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.694499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.375754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.012132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.393037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.908747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.416681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.623748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.144591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.431282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.829494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.258624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.880059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.530034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.166950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.508564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.041996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.560951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.713231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.344704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.558454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.976844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.420692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.999791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.627340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.325551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.591947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.146746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.655169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.824684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.492336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.708461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.076963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.520906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.113515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.779579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.472655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.708813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.253099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.748813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.908316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.679959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:48.863483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.178813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.642617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.244493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:54.972480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.574563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.844883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.379352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.849641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.993248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.783147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.007489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.343085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.742895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.387534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.093399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.655158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:57.943504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.508976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:00.974818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.125614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:03.916595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.162958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.447906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:51.824821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.586616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.222924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.796899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.075357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.692845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.075410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.244249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:04.014208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:49.288700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:50.586824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:52.091703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:53.726068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:55.414588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:56.943026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:58.219770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:28:59.851513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:01.174671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-23T07:29:02.376096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-23T07:29:15.840249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Accompany_ClientActive_LoanApplication_Process_DayApplication_Process_HourBike_OwnedCar_OwnedChild_CountCleint_City_RatingClient_Contact_Work_TagClient_EducationClient_Family_MembersClient_GenderClient_Housing_TypeClient_Income_TypeClient_Marital_StatusClient_OccupationClient_Permanent_Match_TagCredit_BureauDefaultHomephone_TagHouse_OwnIDLoan_Contract_TypeMobile_TagOwn_House_AgePhone_ChangeScore_Source_1Score_Source_2Social_Circle_DefaultWorkphone_Working
Accompany_Client1.0000.0000.0180.0190.0040.0420.0150.0240.0160.0240.0230.0470.0170.0190.0760.0220.0220.0080.0090.0260.0550.0040.0140.0000.0170.0130.0180.0000.0100.014
Active_Loan0.0001.0000.0080.0070.0000.0000.0000.0000.0030.0000.0000.0040.0060.0000.0010.0010.0050.0100.0000.0000.0050.0070.0010.0000.0030.0090.0000.0000.0060.000
Application_Process_Day0.0180.0081.000-0.0250.0000.000-0.0000.0190.0050.0000.0020.0090.0070.0160.0050.0200.0060.0060.0060.0320.0230.0020.0220.0000.018-0.0030.003-0.0000.0020.013
Application_Process_Hour0.0190.007-0.0251.0000.0040.018-0.0030.2150.0250.050-0.0130.0140.0220.0470.0270.0310.022-0.0350.0250.0640.128-0.0010.0410.000-0.1290.0040.0340.1640.0800.075
Bike_Owned0.0040.0000.0000.0041.0000.0000.0060.0000.0030.0040.0000.0040.0000.0000.0030.0100.0000.0000.0000.0000.0000.0000.0050.0000.0100.0000.0070.0000.0070.005
Car_Owned0.0420.0000.0000.0180.0001.0000.0730.0230.0900.0940.1180.3500.0390.1610.1580.2680.0000.0380.0230.0110.0080.0090.0000.0001.0000.0370.0690.0000.0300.008
Child_Count0.0150.000-0.000-0.0030.0060.0731.0000.0200.0500.0100.8120.0240.0000.0550.0820.0180.010-0.0380.0140.0470.000-0.0080.0110.0000.0050.010-0.153-0.018-0.0130.016
Cleint_City_Rating0.0240.0000.0190.2150.0000.0230.0201.0000.0120.0750.0220.0090.0850.1310.0250.0620.0500.0150.0590.0130.0160.0000.0270.0000.1250.0520.0870.0000.1370.094
Client_Contact_Work_Tag0.0160.0030.0050.0250.0030.0900.0500.0121.0000.0330.0680.1330.0380.2200.0660.1270.0250.0120.0280.1100.0340.0000.0070.0000.0570.0200.1190.0000.0810.021
Client_Education0.0240.0000.0000.0500.0040.0940.0100.0750.0331.0000.0240.0200.0420.1030.0550.2170.0340.0270.0630.0130.0290.0000.0650.0140.0960.0220.0750.0040.0420.032
Client_Family_Members0.0230.0000.002-0.0130.0000.1180.8120.0220.0680.0241.0000.0490.0100.0960.1820.0200.029-0.0190.0230.0520.007-0.0060.0310.0000.0070.035-0.098-0.000-0.0090.032
Client_Gender0.0470.0040.0090.0140.0040.3500.0240.0090.1330.0200.0491.0000.0490.1200.1140.4060.0480.0170.0510.0340.0430.0000.0170.0000.0700.0240.2200.0000.0140.024
Client_Housing_Type0.0170.0060.0070.0220.0000.0390.0000.0850.0380.0420.0100.0491.0000.0540.0870.0330.1940.0060.0350.0230.2240.0040.0260.0000.0600.0220.0830.0000.0140.034
Client_Income_Type0.0190.0000.0160.0470.0000.1610.0550.1310.2200.1030.0960.1200.0541.0000.1450.1440.0920.0270.0590.2450.0690.0000.0600.0000.0430.0220.1380.0000.0270.019
Client_Marital_Status0.0760.0010.0050.0270.0030.1580.0820.0250.0660.0550.1820.1140.0870.1451.0000.0650.0590.0180.0270.0690.0500.0000.0460.0000.0540.0410.1140.0000.0060.025
Client_Occupation0.0220.0010.0200.0310.0100.2680.0180.0620.1270.2170.0200.4060.0330.1440.0651.0000.0510.0200.0800.0260.0300.0020.0501.0000.0670.0230.0980.0000.0220.051
Client_Permanent_Match_Tag0.0220.0050.0060.0220.0000.0000.0100.0500.0250.0340.0290.0480.1940.0920.0590.0511.0000.0060.0420.0440.0560.0000.0160.0000.0200.0590.1400.0000.0540.049
Credit_Bureau0.0080.0100.006-0.0350.0000.038-0.0380.0150.0120.027-0.0190.0170.0060.0270.0180.0200.0061.0000.0220.0760.0560.0020.0431.0000.0250.1540.011-0.025-0.0210.031
Default0.0090.0000.0060.0250.0000.0230.0140.0590.0280.0630.0230.0510.0350.0590.0270.0800.0420.0221.0000.0210.0000.0000.0270.0000.0580.0560.1580.0000.0320.025
Homephone_Tag0.0260.0000.0320.0640.0000.0110.0470.0130.1100.0130.0520.0340.0230.2450.0690.0260.0440.0760.0211.0000.1120.0060.0320.0000.0710.0640.0800.0000.0170.290
House_Own0.0550.0050.0230.1280.0000.0080.0000.0160.0340.0290.0070.0430.2240.0690.0500.0300.0560.0560.0000.1121.0000.0000.0690.0000.0240.0490.0790.0000.0150.040
ID0.0040.0070.002-0.0010.0000.009-0.0080.0000.0000.000-0.0060.0000.0040.0000.0000.0020.0000.0020.0000.0060.0001.0000.0080.0000.004-0.000-0.002-0.0060.0030.000
Loan_Contract_Type0.0140.0010.0220.0410.0050.0000.0110.0270.0070.0650.0310.0170.0260.0600.0460.0500.0160.0430.0270.0320.0690.0081.0000.0000.0560.0690.0080.0000.0090.022
Mobile_Tag0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0001.0000.0111.0001.0001.0000.0000.000
Own_House_Age0.0170.0030.018-0.1290.0101.0000.0050.1250.0570.0960.0070.0700.0600.0430.0540.0670.0200.0250.0580.0710.0240.0040.0560.0111.0000.019-0.128-0.131-0.0800.065
Phone_Change0.0130.009-0.0030.0040.0000.0370.0100.0520.0200.0220.0350.0240.0220.0220.0410.0230.0590.1540.0560.0640.049-0.0000.0691.0000.0191.0000.1290.207-0.0000.070
Score_Source_10.0180.0000.0030.0340.0070.069-0.1530.0870.1190.075-0.0980.2200.0830.1380.1140.0980.1400.0110.1580.0800.079-0.0020.0081.000-0.1280.1291.0000.2260.0580.079
Score_Source_20.0000.000-0.0000.1640.0000.000-0.0180.0000.0000.004-0.0000.0000.0000.0000.0000.0000.000-0.0250.0000.0000.000-0.0060.0001.000-0.1310.2070.2261.0000.0970.000
Social_Circle_Default0.0100.0060.0020.0800.0070.030-0.0130.1370.0810.042-0.0090.0140.0140.0270.0060.0220.054-0.0210.0320.0170.0150.0030.0090.000-0.080-0.0000.0580.0971.0000.061
Workphone_Working0.0140.0000.0130.0750.0050.0080.0160.0940.0210.0320.0320.0240.0340.0190.0250.0510.0490.0310.0250.2900.0400.0000.0220.0000.0650.0700.0790.0000.0611.000

Missing values

2025-06-23T07:29:04.647505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-23T07:29:05.459595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-23T07:29:08.075912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
01214250967500.00.01.00.00.061190.553416.85AloneCommercialSecondaryMMaleCLHome0.0286631395710626123383NaN110Sales2.02.06.017.0YesYesSelf-employed0.5680660.478787NaN0.018663.0NaN0
112138936202501.00.01.0NaN0.0152821826.55AloneServiceGraduationMMaleCLHome0.0085751416241297833210.0101NaN2.02.03.010.0YesYesGovernment0.5633600.215068NaNNaNNaNNaN0
212181264180000.00.01.00.01.059527.352788.2AloneServiceGraduation dropoutWMaleCLFamily0.0228167905102NaN331NaN100Realty agents2.02.04.0NaNYesYesSelf-employedNaN0.5527950.3296550.0742277.00.00
312188929157500.00.01.01.00.053870.42295.45AloneRetiredSecondaryMMaleCLHome0.01055623195365243NaN775NaN100NaN2.03.02.015.0YesYesXNANaN0.1351820.631355NaN1700.03.00
412133385337501.00.01.00.02.0133988.43547.35AloneCommercialSecondaryMFemaleCLHome0.020713113662977551640436.0100Laborers4.01.03.0NaNYesYesBusiness Entity Type 30.5081990.3011820.3556390.2021674.01.00
512191614112500.01.01.01.01.013752653.85AloneServiceSecondaryWFemaleCLHome0.01910113881118439103910NaN100Laborers2.02.02.010.0YesYesOtherNaN0.6979280.4206110.0639739.00.00
612128086157501.01.00.01.00.01288353779.55AloneRetiredSecondarySMaleCLHome0.01661221323365243113485510.0100NaN1.02.03.014.0YesYesXNA0.7299130.6025450.5118920.20410.03.00
712215264135000.00.01.01.00.060415.23097.8AloneRetiredSecondaryMMaleCLHome0.00917522493365243126175280NaN101NaN2.02.04.015.0YesYesXNA0.7114680.6575080.549597NaN1687.04.00
812159147135001.01.00.01.01.0450001200.15RelativeCommercialGraduationMFemaleCLHome0.006008NaN78895455266514.0101Sales3.02.04.013.0YesYesSelf-employed0.4757270.6375940.5531650.16701611.00.00
912130547121500.00.00.01.00.016320.151294.65AloneRetiredSecondaryWMaleCLHome0.0166122050736524328344053NaN100NaN1.02.0NaN9.0YesYesXNA0.6822850.0633430.08065NaN533.05.00
IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
12184612204389121500.01.00.01.00.0254701462.05AloneRetiredGraduation dropoutSMaleCLHome0.02516424123.0365243.09523.0795.0NaN100NaN1.02.0NaN9.0YesYesXNA0.720885NaNNaN0.07110.00.00
12184712186941157501.00.01.01.00.026128.81283.85AloneCommercialNaNMMaleCLHomeNaN14025.01107.0507.04514.024.0100ManagersNaN1.05.09.0YesYesBusiness Entity Type 3NaN0.7298250.441836NaN1175.01.00
12184812110723180001.01.00.00.01.027302.42169.9AloneServiceSecondaryMFemaleCLHome0.03579211073.01521.04883.03602.023.0100Sales3.02.02.014.0YesYesHousingNaN0.6252070.306202NaN1718.02.00
12184912183464103500.01.00.00.00.018792.91736.55AloneServiceGraduation dropoutSMaleCLMunicipal0.0100329204.0763.03773.01874.0NaN101Sales1.02.03.011.0YesYesSelf-employed0.1627600.621042NaN0.3340774.0NaN0
12185012136406121500.00.01.00.00.0781922383.65AloneRetiredSecondarySMaleCLHome0.0188523943.0365243.01213.04011.0NaN100NaN1.02.02.011.0YesYesXNANaN0.6782490.2837120.05151581.02.00
12185112207714292500.00.0NaN1.00.01078203165.3RelativeServiceSecondaryMFemaleCLHome0.03132912889.02863.02661.02943.0NaN100Laborers2.02.04.016.0YesNoBusiness Entity Type 2NaN0.1735270.1841160.05770.01.01
12185212173765157500.01.01.00.00.01042563388.05AloneCommercialGraduationMFemaleCLHome0.0182098648.0636.0902.01209.0NaN110Sales2.03.04.012.0YesYesSelf-employedNaN0.3715590.4066170.08254.00.00
1218531210393781000.01.00.01.01.055107.92989.35AloneGovt JobSecondaryMMaleCLHome0.0080689152.01623.03980.0353.0NaN100High skill tech3.03.05.011.0NoNoTrade: type 60.1690490.048079NaNNaN0.0NaN0
12185412170623382501.01.00.01.00.0450002719.35AloneServiceGraduationMFemaleCLHome0.02866310290.0847.0895.02902.04.0100Sales2.02.01.012.0YesYesBusiness Entity Type 30.1827370.1035380.0774990.09790.02.00
1218551210561090001.01.01.01.01.062428.954201.65AloneCommercialSecondarySMaleCLHome0.01802914772.0498.08679.05025.06.0100Managers2.03.04.06.0YesYesBusiness Entity Type 3NaN0.5564140.2985950.1031805.00.00